arXiv:2607. 27987v1 Announce Type: new Abstract: Tensor operations represent a cornerstone of modern scientific computing.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
Vectorizer is a source‑to‑source tool that transforms NumPy programs containing explicit loops into vectorized array operations by rewriting loop bodies from the inside out. It uses array shapes and dataflow analysis to guide a set of rewrite rules that are correct by construction, achieving fast transformations—averaging 0.53 seconds per benchmark. In tests on 150 benchmarks, Vectorizer successfully vectorized 142 directly and 2 with minor edits, producing code that runs on average 74.83× faster than the original loop‑based implementations.
arXiv:2602. 02759v3 Announce Type: replace-cross Abstract: Despite the ubiquity of multiway data across scientific domains, there are few performant and user-friendly methods that fit non-standard nonnegative tensor factorization models tailored to the data at-hand.
arXiv:2409. 17502v2 Announce Type: replace Abstract: Broadcast operations are widely used in scientific computing libraries, yet their mathematical formulation is often implicit and inconsistently represented in machine learning literature.
arXiv:2608.24738v1 Announce Type: new Abstract: Morphological transforms are long-standing tools for shape and mask processing, but the de facto reference implementation in the Python ecosystem, i.e....
Morphological transforms are long-standing tools for shape and mask processing, but the de facto reference implementation in the Python ecosystem, i.e. scipy.ndimage, is CPU-only, single-array, and th...
arXiv:2608. 17135v1 Announce Type: cross Abstract: Tensor networks are powerful formats for compressing large-scale data.